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KASER: Knowledge Amplification by Structured Expert Randomization.

Stuart H Rubin1, S N Jayaram Murthy, Michael H Smith

  • 1SPAWAR Systems Center, San Diego, CA 92152-5001, USA. stuart.rubin@navy.mil

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|December 29, 2004
PubMed
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This paper introduces KASER, a creative expert system capable of advanced reasoning and metaphor-based explanations. KASER demonstrates accelerated learning, with performance varying based on domain symmetry and randomness.

Area of Science:

  • Artificial Intelligence
  • Expert Systems
  • Cognitive Science

Background:

  • Conventional expert systems have limitations in creative reasoning and explanation.
  • The U.S. Navy's SPAWAR Systems Center developed a novel expert system.

Purpose of the Study:

  • To present the third-generation expert system, Knowledge Amplification by Structured Expert Randomization (KASER).
  • To highlight KASER's creative reasoning, learning capabilities, and explanation methods.

Main Methods:

  • KASER utilizes a tree-search mechanism for qualitative creativity.
  • It employs declarative knowledge representation with object trees and inheritance.
  • The system computes with words and phrases, incorporating metaphor-based explanations.

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Main Results:

  • KASER demonstrates deductive, inductive, and mixed derivations.
  • It exhibits an accelerated learning capability, influenced by domain symmetry.
  • Metaphor-based explanations augment conventional system capabilities.

Conclusions:

  • KASER represents a significant advancement in creative expert systems.
  • Its performance is adaptable, with learning efficiency tied to domain compressibility (symmetry vs. randomness).
  • The system offers enhanced creative suggestions and explanations.